R Notebook: estimation of gas costs using the measure marginal method; for Gas Cost Estimator; for a single given env
In this script we conduct the estimation for the
measure_marginal approach for a single given env =
besu.
PROGRAMS=pg_marginal_full5_c50_step1_shuffle SAMPLESIZE=50 NSAMPLES=4.
Expected a result file besu_pg_marginal_full5_c50_step1_shuffle_50_4.csv.
programs = read.csv(paste("stage3/", program_set_codename, ".csv", sep=""))
results = load_data_set(env, program_set_codename, measurement_codename)
# besu may have additional columns with gc stats
results = results[, c("program_id", "sample_id", "run_id", "measure_total_time_ns", "measure_total_timer_time_ns", "env")]
# TODO geth short-circuits zero length programs, resulting in zero timing somehow. Drop these more elegantly, not based on measure_total_time_ns
results = results[which(results$measure_total_time_ns != 0), ]
all_envs = c(env)measurements = sqldf("SELECT opcode, op_count, sample_id, run_id, measure_total_time_ns, env, results.program_id
FROM results
INNER JOIN
programs ON(results.program_id = programs.program_id)")
measurements$opcode = factor(measurements$opcode, levels=unique(programs$opcode))
head(measurements)## opcode op_count sample_id run_id measure_total_time_ns env program_id
## 1 ADD 27 0 0 7683054 besu ADD_27
## 2 ADD 27 0 1 7921775 besu ADD_27
## 3 ADD 27 0 2 7844004 besu ADD_27
## 4 ADD 27 0 3 7794454 besu ADD_27
## 5 ADD 27 0 4 7725708 besu ADD_27
## 6 ADD 27 0 5 7929989 besu ADD_27Switch removed_outliers to FALSE to see the
comparison.
boxplot(measurements[which(measurements$env == env), 'measure_total_time_ns'] ~ measurements[which(measurements$env == env), 'opcode'], las=2, outline=TRUE, log='y', main=paste(env, 'all'))
if (removed_outliers) {
measurements = remove_compare_outliers(measurements, 'measure_total_time_ns', all_envs)
}# For a subset of the `measurements` data frame, fits a bimodal distribution model and corrects the
# data by bringing the "top-mode" cluster down to the "bottom-mode" cluster.
correct_bimodal <- function(df) {
mix_model = normalmixEM(df$measure_total_time_ns)
print(summary(mix_model))
plot(mix_model,which=2)
mode_distance = abs(mix_model$mu[2] - mix_model$mu[1])
mode_midpoint = (mix_model$mu[2] + mix_model$mu[1]) / 2
over_threshold = which(df$measure_total_time_ns > mode_midpoint)
df[over_threshold, "measure_total_time_ns"] = df[over_threshold, "measure_total_time_ns"] - mode_distance
return(df)
}
# Performs the `measure_marginal` estimation procedure for a given slice of the data.
# Prints the diagnostics and plots the models.
compute_all <- function(opcode, env, plots, bimodal_opcodes, use_median) {
if (missing(bimodal_opcodes)) {
bimodal_opcodes = c()
}
if (missing(plots)) {
plots = "scatter"
}
if (missing(use_median)) {
use_median = FALSE
}
print(c(opcode, env))
df = measurements[which(measurements$opcode==opcode & measurements$env==env),]
if (opcode %in% bimodal_opcodes) {
par(mfrow=c(1,2))
boxplot(measure_total_time_ns ~ op_count, data=df, las=2, outline=removed_outliers)
title(main=paste(env, opcode))
# correct_bimodal plots the second plot inside
df = correct_bimodal(df)
}
if (use_median) {
f = median
} else {
f = mean
}
df_mean = aggregate(measure_total_time_ns ~ op_count * env, df, f)
model_mean = lm(measure_total_time_ns ~ op_count, data=df_mean)
print(summary(model_mean))
slope = model_mean$coefficients[['op_count']]
stderr = summary(model_mean)$coefficients['op_count','Std. Error']
if (plots == "scatter" | plots == "all") {
par(mfrow=c(1,1))
boxplot(measure_total_time_ns ~ op_count, data=df, las=2, outline=removed_outliers)
rounded_slope = round(slope, 3)
rounded_p = round(summary(model_mean)$coefficients['op_count','Pr(>|t|)'], 3)
rounded_stderr = round(stderr, 3)
title(main=paste(env, opcode, rounded_slope, "p_value:", rounded_p, "StdErr:", rounded_stderr))
abline(model_mean, col="red")
}
if (plots == "diagnostics" | plots == "all") {
par(mfrow=c(2,2))
plot(model_mean)
}
list("slope" = slope, "stderr" = stderr)
}
extract_opcodes <- function() {
unique(measurements$opcode)
}all_opcodes = extract_opcodes()
# initialize the data frame to hold the results
estimates = data.frame(matrix(ncol = 4, nrow = 0))
colnames(estimates) <- c('op', 'estimate_marginal_ns', 'estimate_marginal_ns_stderr', 'env')Every sample starts with a fresh evm instance. We investigate whether the results may depend on the time from evm start - related to run_id. To avoid being overrun by the number of images, all op_count for a given run_id are are placed, so values are not centered. That should not an issue.
for (opcode in all_opcodes) {
boxplot(measure_total_time_ns~run_id,data=measurements[measurements$opcode == opcode,], main=opcode)
}
Now we can investigate the linear regressions.
if (env == 'evmone') {
bimodals = all_opcodes[which(grepl("PUSH", all_opcodes) & all_opcodes != "PUSH1" | all_opcodes == "JUMP")]
} else {
bimodals = c()
}
for (opcode in all_opcodes) {
estimate = compute_all(opcode=opcode, env=env, use_median=TRUE, bimodal_opcodes=bimodals, plots='all')
estimates[nrow(estimates) + 1, ] = c(opcode, estimate, env)
}## [1] "ADD" "besu"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -52279 -10915 -2341 8374 37758
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3502078.1 4966.0 705.2 <0.0000000000000002 ***
## op_count 32687.8 171.2 191.0 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 17990 on 49 degrees of freedom
## Multiple R-squared: 0.9987, Adjusted R-squared: 0.9986
## F-statistic: 3.647e+04 on 1 and 49 DF, p-value: < 0.00000000000000022
## [1] "MUL" "besu"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -55451 -10295 532 10691 69405
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3505621.5 5450.4 643.2 <0.0000000000000002 ***
## op_count 104188.9 187.9 554.6 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 19750 on 49 degrees of freedom
## Multiple R-squared: 0.9998, Adjusted R-squared: 0.9998
## F-statistic: 3.076e+05 on 1 and 49 DF, p-value: < 0.00000000000000022
## [1] "SUB" "besu"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -63734 -7823 -189 11288 42546
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3509751.3 4604.5 762.2 <0.0000000000000002 ***
## op_count 76324.5 158.7 480.9 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 16680 on 49 degrees of freedom
## Multiple R-squared: 0.9998, Adjusted R-squared: 0.9998
## F-statistic: 2.313e+05 on 1 and 49 DF, p-value: < 0.00000000000000022
## [1] "DIV" "besu"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -73836 -8271 -787 11939 56189
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3506457.2 5571.7 629.3 <0.0000000000000002 ***
## op_count 31828.8 192.1 165.7 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 20190 on 49 degrees of freedom
## Multiple R-squared: 0.9982, Adjusted R-squared: 0.9982
## F-statistic: 2.747e+04 on 1 and 49 DF, p-value: < 0.00000000000000022
## [1] "SDIV" "besu"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -300791 -54512 -10517 30828 298442
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3728227.5 27723.3 134.48 <0.0000000000000002 ***
## op_count 68287.9 955.6 71.46 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 100500 on 49 degrees of freedom
## Multiple R-squared: 0.9905, Adjusted R-squared: 0.9903
## F-statistic: 5107 on 1 and 49 DF, p-value: < 0.00000000000000022
## [1] "MOD" "besu"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -302978 -55135 -11809 34289 282087
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3737434.7 28240.5 132.34 <0.0000000000000002 ***
## op_count 59731.7 973.4 61.36 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 102300 on 49 degrees of freedom
## Multiple R-squared: 0.9872, Adjusted R-squared: 0.9869
## F-statistic: 3765 on 1 and 49 DF, p-value: < 0.00000000000000022
## [1] "SMOD" "besu"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -299344 -42996 -12236 25675 390088
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3730418 29134 128.04 <0.0000000000000002 ***
## op_count 59513 1004 59.26 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 105600 on 49 degrees of freedom
## Multiple R-squared: 0.9862, Adjusted R-squared: 0.986
## F-statistic: 3512 on 1 and 49 DF, p-value: < 0.00000000000000022
## [1] "ADDMOD" "besu"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -351965 -66341 -15382 41796 397857
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3794320 33818 112.20 <0.0000000000000002 ***
## op_count 72659 1166 62.33 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 122500 on 49 degrees of freedom
## Multiple R-squared: 0.9875, Adjusted R-squared: 0.9873
## F-statistic: 3885 on 1 and 49 DF, p-value: < 0.00000000000000022
## [1] "MULMOD" "besu"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -357883 -74380 -31088 58415 354104
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3823473 40151 95.23 <0.0000000000000002 ***
## op_count 76193 1384 55.05 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 145500 on 49 degrees of freedom
## Multiple R-squared: 0.9841, Adjusted R-squared: 0.9838
## F-statistic: 3031 on 1 and 49 DF, p-value: < 0.00000000000000022
## [1] "EXP" "besu"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -215463 -46176 829 48189 153173
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3629179.0 22340.8 162.4 <0.0000000000000002 ***
## op_count 242949.4 770.1 315.5 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 80950 on 49 degrees of freedom
## Multiple R-squared: 0.9995, Adjusted R-squared: 0.9995
## F-statistic: 9.953e+04 on 1 and 49 DF, p-value: < 0.00000000000000022
## [1] "SIGNEXTEND" "besu"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -57290 -11336 -255 12911 43952
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3516202.5 5592.3 628.8 <0.0000000000000002 ***
## op_count 97857.2 192.8 507.7 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 20260 on 49 degrees of freedom
## Multiple R-squared: 0.9998, Adjusted R-squared: 0.9998
## F-statistic: 2.577e+05 on 1 and 49 DF, p-value: < 0.00000000000000022
## [1] "LT" "besu"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -60897 -10665 1403 12589 45577
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3492540.3 5617.3 621.7 <0.0000000000000002 ***
## op_count 107354.4 193.6 554.4 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 20350 on 49 degrees of freedom
## Multiple R-squared: 0.9998, Adjusted R-squared: 0.9998
## F-statistic: 3.074e+05 on 1 and 49 DF, p-value: < 0.00000000000000022
## [1] "GT" "besu"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -48256 -16250 3219 14508 39123
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3489786.8 5592.9 624.0 <0.0000000000000002 ***
## op_count 107475.8 192.8 557.5 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 20270 on 49 degrees of freedom
## Multiple R-squared: 0.9998, Adjusted R-squared: 0.9998
## F-statistic: 3.108e+05 on 1 and 49 DF, p-value: < 0.00000000000000022
## [1] "SLT" "besu"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -46201 -12266 -4127 9837 74141
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3508292 5569 629.9 <0.0000000000000002 ***
## op_count 23422 192 122.0 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 20180 on 49 degrees of freedom
## Multiple R-squared: 0.9967, Adjusted R-squared: 0.9967
## F-statistic: 1.489e+04 on 1 and 49 DF, p-value: < 0.00000000000000022
## [1] "SGT" "besu"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -47859 -7333 -1205 10442 21816
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3504719.7 3910.5 896.2 <0.0000000000000002 ***
## op_count 23337.9 134.8 173.1 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 14170 on 49 degrees of freedom
## Multiple R-squared: 0.9984, Adjusted R-squared: 0.9983
## F-statistic: 2.998e+04 on 1 and 49 DF, p-value: < 0.00000000000000022
## [1] "EQ" "besu"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -32196 -7960 -746 9705 32817
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3486603.5 3913.0 891.0 <0.0000000000000002 ***
## op_count 74121.1 134.9 549.5 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 14180 on 49 degrees of freedom
## Multiple R-squared: 0.9998, Adjusted R-squared: 0.9998
## F-statistic: 3.02e+05 on 1 and 49 DF, p-value: < 0.00000000000000022
## [1] "ISZERO" "besu"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -38002 -8311 88 8087 32255
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3489316.3 3949.8 883.4 <0.0000000000000002 ***
## op_count 40797.9 136.1 299.7 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 14310 on 49 degrees of freedom
## Multiple R-squared: 0.9995, Adjusted R-squared: 0.9994
## F-statistic: 8.98e+04 on 1 and 49 DF, p-value: < 0.00000000000000022
## [1] "AND" "besu"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -29464.6 -10019.7 783.5 9156.1 28637.0
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3489035.1 3584.2 973.4 <0.0000000000000002 ***
## op_count 75815.6 123.5 613.7 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 12990 on 49 degrees of freedom
## Multiple R-squared: 0.9999, Adjusted R-squared: 0.9999
## F-statistic: 3.766e+05 on 1 and 49 DF, p-value: < 0.00000000000000022
## [1] "OR" "besu"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -63511 -8493 854 11278 54950
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3490237.8 5109.7 683.1 <0.0000000000000002 ***
## op_count 75651.6 176.1 429.5 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 18510 on 49 degrees of freedom
## Multiple R-squared: 0.9997, Adjusted R-squared: 0.9997
## F-statistic: 1.845e+05 on 1 and 49 DF, p-value: < 0.00000000000000022
## [1] "XOR" "besu"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -44603 -8680 3459 8583 25998
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3491596.0 4019.6 868.6 <0.0000000000000002 ***
## op_count 75642.8 138.6 546.0 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 14560 on 49 degrees of freedom
## Multiple R-squared: 0.9998, Adjusted R-squared: 0.9998
## F-statistic: 2.981e+05 on 1 and 49 DF, p-value: < 0.00000000000000022
## [1] "NOT" "besu"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -28142 -9419 -1214 6787 108207
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3491108.3 5662.4 616.5 <0.0000000000000002 ***
## op_count 43165.7 195.2 221.2 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 20520 on 49 degrees of freedom
## Multiple R-squared: 0.999, Adjusted R-squared: 0.999
## F-statistic: 4.891e+04 on 1 and 49 DF, p-value: < 0.00000000000000022
## [1] "BYTE" "besu"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -68704 -9613 3019 11994 32518
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3492218.9 4805.3 726.7 <0.0000000000000002 ***
## op_count 83253.8 165.6 502.6 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 17410 on 49 degrees of freedom
## Multiple R-squared: 0.9998, Adjusted R-squared: 0.9998
## F-statistic: 2.526e+05 on 1 and 49 DF, p-value: < 0.00000000000000022
## [1] "SHL" "besu"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -43553 -22684 -13333 -2226 428927
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3511025.9 19557.5 179.5 <0.0000000000000002 ***
## op_count 54400.7 674.1 80.7 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 70860 on 49 degrees of freedom
## Multiple R-squared: 0.9925, Adjusted R-squared: 0.9924
## F-statistic: 6512 on 1 and 49 DF, p-value: < 0.00000000000000022
## [1] "SHR" "besu"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -114605 -30951 -18830 -7441 275285
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3537638.8 22131.3 159.85 <0.0000000000000002 ***
## op_count 57479.8 762.8 75.35 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 80190 on 49 degrees of freedom
## Multiple R-squared: 0.9914, Adjusted R-squared: 0.9913
## F-statistic: 5677 on 1 and 49 DF, p-value: < 0.00000000000000022
## [1] "SAR" "besu"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -91931 -36025 -26901 -8980 374010
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3541944.8 25459.0 139.1 <0.0000000000000002 ***
## op_count 55724.5 877.5 63.5 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 92250 on 49 degrees of freedom
## Multiple R-squared: 0.988, Adjusted R-squared: 0.9877
## F-statistic: 4032 on 1 and 49 DF, p-value: < 0.00000000000000022
## [1] "ADDRESS" "besu"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -97606 -45265 -8562 39172 140926
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1515055.1 16066.1 94.30 <0.0000000000000002 ***
## op_count 9315.4 553.8 16.82 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 58210 on 49 degrees of freedom
## Multiple R-squared: 0.8524, Adjusted R-squared: 0.8494
## F-statistic: 283 on 1 and 49 DF, p-value: < 0.00000000000000022
## [1] "ORIGIN" "besu"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -77141 -40379 -5200 37134 105890
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1527943 13577 112.54 <0.0000000000000002 ***
## op_count 8140 468 17.39 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 49190 on 49 degrees of freedom
## Multiple R-squared: 0.8606, Adjusted R-squared: 0.8578
## F-statistic: 302.6 on 1 and 49 DF, p-value: < 0.00000000000000022
## [1] "CALLER" "besu"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -98256 -36489 -353 32388 105683
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1528833.9 13164.8 116.13 <0.0000000000000002 ***
## op_count 8799.1 453.8 19.39 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 47700 on 49 degrees of freedom
## Multiple R-squared: 0.8847, Adjusted R-squared: 0.8824
## F-statistic: 376 on 1 and 49 DF, p-value: < 0.00000000000000022
## [1] "CALLVALUE" "besu"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -79438 -46909 -19387 35630 192806
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1510745.9 16846.9 89.67 <0.0000000000000002 ***
## op_count 9111.1 580.7 15.69 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 61040 on 49 degrees of freedom
## Multiple R-squared: 0.834, Adjusted R-squared: 0.8306
## F-statistic: 246.2 on 1 and 49 DF, p-value: < 0.00000000000000022
## [1] "CALLDATALOAD" "besu"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -66946 -15154 -1713 14467 77817
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 5312799.3 7780.7 682.8 <0.0000000000000002 ***
## op_count 56491.2 268.2 210.6 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 28190 on 49 degrees of freedom
## Multiple R-squared: 0.9989, Adjusted R-squared: 0.9989
## F-statistic: 4.437e+04 on 1 and 49 DF, p-value: < 0.00000000000000022
## [1] "CALLDATASIZE" "besu"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -40675 -14772 -5206 -1023 196442
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1485551.7 10006.9 148.45 <0.0000000000000002 ***
## op_count 10008.6 344.9 29.02 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 36260 on 49 degrees of freedom
## Multiple R-squared: 0.945, Adjusted R-squared: 0.9439
## F-statistic: 842 on 1 and 49 DF, p-value: < 0.00000000000000022
## [1] "CALLDATACOPY" "besu"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -171930 -57593 -16076 66450 152729
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3762493.1 22714.3 165.64 <0.0000000000000002 ***
## op_count 39900.6 782.9 50.96 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 82300 on 49 degrees of freedom
## Multiple R-squared: 0.9815, Adjusted R-squared: 0.9811
## F-statistic: 2597 on 1 and 49 DF, p-value: < 0.00000000000000022
## [1] "CODESIZE" "besu"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -42597 -13506 -2060 5355 79603
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1483679.0 5906.3 251.21 <0.0000000000000002 ***
## op_count 10206.8 203.6 50.14 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 21400 on 49 degrees of freedom
## Multiple R-squared: 0.9809, Adjusted R-squared: 0.9805
## F-statistic: 2514 on 1 and 49 DF, p-value: < 0.00000000000000022
## [1] "CODECOPY" "besu"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -163422 -72237 6348 65283 166196
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3702314.7 24353.4 152.0 <0.0000000000000002 ***
## op_count 40793.8 839.4 48.6 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 88240 on 49 degrees of freedom
## Multiple R-squared: 0.9797, Adjusted R-squared: 0.9793
## F-statistic: 2362 on 1 and 49 DF, p-value: < 0.00000000000000022
## [1] "GASPRICE" "besu"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -74252 -38229 -21211 41082 100190
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1528059.1 13648.2 111.96 <0.0000000000000002 ***
## op_count 8510.1 470.4 18.09 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 49450 on 49 degrees of freedom
## Multiple R-squared: 0.8698, Adjusted R-squared: 0.8671
## F-statistic: 327.2 on 1 and 49 DF, p-value: < 0.00000000000000022
## [1] "RETURNDATASIZE" "besu"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -65413 -36503 -9150 31580 128331
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1513785.2 12883.8 117.5 <0.0000000000000002 ***
## op_count 9548.7 444.1 21.5 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 46680 on 49 degrees of freedom
## Multiple R-squared: 0.9042, Adjusted R-squared: 0.9022
## F-statistic: 462.3 on 1 and 49 DF, p-value: < 0.00000000000000022
## [1] "RETURNDATACOPY" "besu"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -244051 -98990 6665 85329 203531
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9114953 33460 272.41 <0.0000000000000002 ***
## op_count 47436 1153 41.13 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 121200 on 49 degrees of freedom
## Multiple R-squared: 0.9718, Adjusted R-squared: 0.9713
## F-statistic: 1692 on 1 and 49 DF, p-value: < 0.00000000000000022
## [1] "COINBASE" "besu"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -83971 -34566 -5112 20151 103612
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1497668.8 13298.8 112.62 <0.0000000000000002 ***
## op_count 9323.5 458.4 20.34 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 48190 on 49 degrees of freedom
## Multiple R-squared: 0.8941, Adjusted R-squared: 0.8919
## F-statistic: 413.7 on 1 and 49 DF, p-value: < 0.00000000000000022
## [1] "TIMESTAMP" "besu"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -34352 -13233 -4492 10999 59492
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1487837.9 5531.5 268.98 <0.0000000000000002 ***
## op_count 9833.2 190.7 51.57 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 20040 on 49 degrees of freedom
## Multiple R-squared: 0.9819, Adjusted R-squared: 0.9815
## F-statistic: 2660 on 1 and 49 DF, p-value: < 0.00000000000000022
## [1] "NUMBER" "besu"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -54000 -27378 -16441 5913 290466
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1517474.3 15776.4 96.19 <0.0000000000000002 ***
## op_count 9719.0 543.8 17.87 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 57160 on 49 degrees of freedom
## Multiple R-squared: 0.867, Adjusted R-squared: 0.8643
## F-statistic: 319.4 on 1 and 49 DF, p-value: < 0.00000000000000022
## [1] "DIFFICULTY" "besu"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -66700 -44024 -11775 18498 128725
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1518159.3 15226.9 99.70 <0.0000000000000002 ***
## op_count 8890.9 524.9 16.94 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 55170 on 49 degrees of freedom
## Multiple R-squared: 0.8541, Adjusted R-squared: 0.8512
## F-statistic: 287 on 1 and 49 DF, p-value: < 0.00000000000000022
## [1] "GASLIMIT" "besu"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -57181 -21790 -10626 6531 128124
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1498426.8 10830.1 138.36 <0.0000000000000002 ***
## op_count 9973.0 373.3 26.72 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 39240 on 49 degrees of freedom
## Multiple R-squared: 0.9358, Adjusted R-squared: 0.9344
## F-statistic: 713.7 on 1 and 49 DF, p-value: < 0.00000000000000022
## [1] "CHAINID" "besu"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -86959 -48662 -10408 38690 141022
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1537545.4 16485.7 93.27 <0.0000000000000002 ***
## op_count 8624.7 568.2 15.18 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 59730 on 49 degrees of freedom
## Multiple R-squared: 0.8246, Adjusted R-squared: 0.821
## F-statistic: 230.4 on 1 and 49 DF, p-value: < 0.00000000000000022
## [1] "SELFBALANCE" "besu"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -830562 -12268 21371 63344 137793
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2329844 38542 60.45 <0.0000000000000002 ***
## op_count 370962 1328 279.23 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 139700 on 49 degrees of freedom
## Multiple R-squared: 0.9994, Adjusted R-squared: 0.9994
## F-statistic: 7.797e+04 on 1 and 49 DF, p-value: < 0.00000000000000022
## [1] "POP" "besu"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -195123 -103831 -19290 66653 610822
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1863264 40898 45.56 <0.0000000000000002 ***
## op_count 20733 1410 14.71 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 148200 on 49 degrees of freedom
## Multiple R-squared: 0.8153, Adjusted R-squared: 0.8115
## F-statistic: 216.3 on 1 and 49 DF, p-value: < 0.00000000000000022
## [1] "MLOAD" "besu"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -37804 -13698 -3373 13829 61963
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 5328581.2 5799.4 918.8 <0.0000000000000002 ***
## op_count 20836.6 199.9 104.2 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 21010 on 49 degrees of freedom
## Multiple R-squared: 0.9955, Adjusted R-squared: 0.9954
## F-statistic: 1.087e+04 on 1 and 49 DF, p-value: < 0.00000000000000022
## [1] "MSTORE" "besu"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -184786 -50291 -19881 42908 169139
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3757432.9 23274.9 161.4 <0.0000000000000002 ***
## op_count 39314.1 802.3 49.0 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 84330 on 49 degrees of freedom
## Multiple R-squared: 0.98, Adjusted R-squared: 0.9796
## F-statistic: 2401 on 1 and 49 DF, p-value: < 0.00000000000000022
## [1] "MSTORE8" "besu"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -188913 -51465 -20236 54883 218136
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3745267.5 24345.6 153.84 <0.0000000000000002 ***
## op_count 24316.7 839.2 28.98 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 88210 on 49 degrees of freedom
## Multiple R-squared: 0.9449, Adjusted R-squared: 0.9437
## F-statistic: 839.7 on 1 and 49 DF, p-value: < 0.00000000000000022
## [1] "JUMP" "besu"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -45182 -25981 -7384 19685 85791
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1458714 9920 147.04 <0.0000000000000002 ***
## op_count 13647 342 39.91 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 35950 on 49 degrees of freedom
## Multiple R-squared: 0.9702, Adjusted R-squared: 0.9695
## F-statistic: 1593 on 1 and 49 DF, p-value: < 0.00000000000000022
## [1] "JUMPI" "besu"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -91950 -17627 -1875 22934 180197
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3453745.6 11629.6 297.0 <0.0000000000000002 ***
## op_count 15073.5 400.9 37.6 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 42140 on 49 degrees of freedom
## Multiple R-squared: 0.9665, Adjusted R-squared: 0.9658
## F-statistic: 1414 on 1 and 49 DF, p-value: < 0.00000000000000022
## [1] "PC" "besu"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -43561 -15380 -2953 10998 105786
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1492752.0 7813.5 191.05 <0.0000000000000002 ***
## op_count 9567.3 269.3 35.52 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 28310 on 49 degrees of freedom
## Multiple R-squared: 0.9626, Adjusted R-squared: 0.9619
## F-statistic: 1262 on 1 and 49 DF, p-value: < 0.00000000000000022
## [1] "MSIZE" "besu"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -85306 -35745 -9146 41258 94993
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1532337.1 13129.2 116.71 <0.0000000000000002 ***
## op_count 9056.1 452.6 20.01 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 47570 on 49 degrees of freedom
## Multiple R-squared: 0.891, Adjusted R-squared: 0.8888
## F-statistic: 400.5 on 1 and 49 DF, p-value: < 0.00000000000000022
## [1] "GAS" "besu"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -40580 -18235 -6048 5114 210899
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1494062.1 10343.1 144.45 <0.0000000000000002 ***
## op_count 10249.0 356.5 28.75 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 37480 on 49 degrees of freedom
## Multiple R-squared: 0.944, Adjusted R-squared: 0.9429
## F-statistic: 826.4 on 1 and 49 DF, p-value: < 0.00000000000000022
## [1] "JUMPDEST" "besu"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -20165.1 -7840.2 -364.2 7119.0 24590.7
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 233128.5 2995.8 77.82 <0.0000000000000002 ***
## op_count 7455.7 103.3 72.20 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 10850 on 49 degrees of freedom
## Multiple R-squared: 0.9907, Adjusted R-squared: 0.9905
## F-statistic: 5213 on 1 and 49 DF, p-value: < 0.00000000000000022
## [1] "PUSH1" "besu"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -39231 -24421 -7196 8152 87366
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1488709.2 8916.3 166.97 <0.0000000000000002 ***
## op_count 10279.8 307.3 33.45 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 32310 on 49 degrees of freedom
## Multiple R-squared: 0.958, Adjusted R-squared: 0.9572
## F-statistic: 1119 on 1 and 49 DF, p-value: < 0.00000000000000022
## [1] "PUSH2" "besu"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -51031 -21474 -3245 9785 178301
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1501368.5 9977.1 150.5 <0.0000000000000002 ***
## op_count 9630.9 343.9 28.0 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 36150 on 49 degrees of freedom
## Multiple R-squared: 0.9412, Adjusted R-squared: 0.94
## F-statistic: 784.3 on 1 and 49 DF, p-value: < 0.00000000000000022
## [1] "PUSH3" "besu"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -57534 -22934 -11582 3671 128700
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1499118.3 11841.3 126.60 <0.0000000000000002 ***
## op_count 10198.3 408.2 24.99 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 42910 on 49 degrees of freedom
## Multiple R-squared: 0.9272, Adjusted R-squared: 0.9257
## F-statistic: 624.3 on 1 and 49 DF, p-value: < 0.00000000000000022
## [1] "PUSH4" "besu"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -39734 -22798 -10425 7013 214329
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1472698.3 11352.4 129.72 <0.0000000000000002 ***
## op_count 10858.8 391.3 27.75 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 41130 on 49 degrees of freedom
## Multiple R-squared: 0.9402, Adjusted R-squared: 0.939
## F-statistic: 770.1 on 1 and 49 DF, p-value: < 0.00000000000000022
## [1] "PUSH5" "besu"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -54336 -27249 -11312 14319 119982
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1500548.4 11119.2 134.95 <0.0000000000000002 ***
## op_count 9979.7 383.3 26.04 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 40290 on 49 degrees of freedom
## Multiple R-squared: 0.9326, Adjusted R-squared: 0.9312
## F-statistic: 678 on 1 and 49 DF, p-value: < 0.00000000000000022
## [1] "PUSH6" "besu"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -55537 -19179 -5047 6888 101739
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1489670.9 9536.3 156.21 <0.0000000000000002 ***
## op_count 10360.8 328.7 31.52 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 34550 on 49 degrees of freedom
## Multiple R-squared: 0.953, Adjusted R-squared: 0.952
## F-statistic: 993.5 on 1 and 49 DF, p-value: < 0.00000000000000022
## [1] "PUSH7" "besu"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -61446 -34051 -9536 14684 210191
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1506749.9 13698.7 109.99 <0.0000000000000002 ***
## op_count 10051.4 472.2 21.29 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 49640 on 49 degrees of freedom
## Multiple R-squared: 0.9024, Adjusted R-squared: 0.9004
## F-statistic: 453.1 on 1 and 49 DF, p-value: < 0.00000000000000022
## [1] "PUSH8" "besu"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -44018 -22828 -8217 7107 140498
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1491647.8 9800.3 152.20 <0.0000000000000002 ***
## op_count 10137.3 337.8 30.01 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 35510 on 49 degrees of freedom
## Multiple R-squared: 0.9484, Adjusted R-squared: 0.9473
## F-statistic: 900.5 on 1 and 49 DF, p-value: < 0.00000000000000022
## [1] "PUSH9" "besu"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -53101 -18305 -6946 6337 100329
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1496077.7 8184.2 182.80 <0.0000000000000002 ***
## op_count 9936.3 282.1 35.22 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 29650 on 49 degrees of freedom
## Multiple R-squared: 0.962, Adjusted R-squared: 0.9612
## F-statistic: 1241 on 1 and 49 DF, p-value: < 0.00000000000000022
## [1] "PUSH10" "besu"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -47220 -17604 -1448 9342 99198
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1496659.5 8146.8 183.71 <0.0000000000000002 ***
## op_count 10043.4 280.8 35.77 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 29520 on 49 degrees of freedom
## Multiple R-squared: 0.9631, Adjusted R-squared: 0.9624
## F-statistic: 1279 on 1 and 49 DF, p-value: < 0.00000000000000022
## [1] "PUSH11" "besu"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -49018 -23148 -2026 9028 144972
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1483763.7 9393.8 157.95 <0.0000000000000002 ***
## op_count 10430.8 323.8 32.21 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 34040 on 49 degrees of freedom
## Multiple R-squared: 0.9549, Adjusted R-squared: 0.954
## F-statistic: 1038 on 1 and 49 DF, p-value: < 0.00000000000000022
## [1] "PUSH12" "besu"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -56896 -26590 -12540 14560 169936
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1502261 12938 116.11 <0.0000000000000002 ***
## op_count 10030 446 22.49 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 46880 on 49 degrees of freedom
## Multiple R-squared: 0.9117, Adjusted R-squared: 0.9099
## F-statistic: 505.8 on 1 and 49 DF, p-value: < 0.00000000000000022
## [1] "PUSH13" "besu"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -40152 -13960 -4360 7653 75255
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1481522.7 6808.5 217.60 <0.0000000000000002 ***
## op_count 10321.4 234.7 43.98 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 24670 on 49 degrees of freedom
## Multiple R-squared: 0.9753, Adjusted R-squared: 0.9748
## F-statistic: 1934 on 1 and 49 DF, p-value: < 0.00000000000000022
## [1] "PUSH14" "besu"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -63710 -18640 -8375 5222 175501
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1487054.9 10911.2 136.29 <0.0000000000000002 ***
## op_count 10287.4 376.1 27.35 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 39540 on 49 degrees of freedom
## Multiple R-squared: 0.9385, Adjusted R-squared: 0.9373
## F-statistic: 748.2 on 1 and 49 DF, p-value: < 0.00000000000000022
## [1] "PUSH15" "besu"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -53532 -31061 -17539 11755 266443
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1510426.5 15762.9 95.82 <0.0000000000000002 ***
## op_count 9980.0 543.3 18.37 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 57110 on 49 degrees of freedom
## Multiple R-squared: 0.8732, Adjusted R-squared: 0.8706
## F-statistic: 337.4 on 1 and 49 DF, p-value: < 0.00000000000000022
## [1] "PUSH16" "besu"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -52749 -23341 -4688 10760 196443
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1493909.1 11033.7 135.40 <0.0000000000000002 ***
## op_count 10147.6 380.3 26.68 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 39980 on 49 degrees of freedom
## Multiple R-squared: 0.9356, Adjusted R-squared: 0.9343
## F-statistic: 711.9 on 1 and 49 DF, p-value: < 0.00000000000000022
## [1] "PUSH17" "besu"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -62287 -24752 -11024 9099 152387
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1483965.0 11740.2 126.40 <0.0000000000000002 ***
## op_count 10621.4 404.7 26.25 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 42540 on 49 degrees of freedom
## Multiple R-squared: 0.9336, Adjusted R-squared: 0.9322
## F-statistic: 688.9 on 1 and 49 DF, p-value: < 0.00000000000000022
## [1] "PUSH18" "besu"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -52968 -18171 -4718 13190 99155
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1497124.6 8924.3 167.76 <0.0000000000000002 ***
## op_count 10051.3 307.6 32.67 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 32340 on 49 degrees of freedom
## Multiple R-squared: 0.9561, Adjusted R-squared: 0.9552
## F-statistic: 1068 on 1 and 49 DF, p-value: < 0.00000000000000022
## [1] "PUSH19" "besu"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -58569 -27074 -13444 9963 173573
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1500107.4 12804.7 117.15 <0.0000000000000002 ***
## op_count 10078.2 441.4 22.83 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 46400 on 49 degrees of freedom
## Multiple R-squared: 0.9141, Adjusted R-squared: 0.9123
## F-statistic: 521.4 on 1 and 49 DF, p-value: < 0.00000000000000022
## [1] "PUSH20" "besu"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -53199 -23199 -11243 11745 264275
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1485322.2 13736.1 108.13 <0.0000000000000002 ***
## op_count 10450.8 473.5 22.07 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 49770 on 49 degrees of freedom
## Multiple R-squared: 0.9086, Adjusted R-squared: 0.9068
## F-statistic: 487.2 on 1 and 49 DF, p-value: < 0.00000000000000022
## [1] "PUSH21" "besu"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -55368 -25028 -13471 9088 143052
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1495836.4 11997.3 124.68 <0.0000000000000002 ***
## op_count 10112.5 413.5 24.45 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 43470 on 49 degrees of freedom
## Multiple R-squared: 0.9243, Adjusted R-squared: 0.9227
## F-statistic: 598 on 1 and 49 DF, p-value: < 0.00000000000000022
## [1] "PUSH22" "besu"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -52793 -18437 -6175 9081 148220
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1487811 10155 146.51 <0.0000000000000002 ***
## op_count 10406 350 29.73 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 36790 on 49 degrees of freedom
## Multiple R-squared: 0.9475, Adjusted R-squared: 0.9464
## F-statistic: 883.7 on 1 and 49 DF, p-value: < 0.00000000000000022
## [1] "PUSH23" "besu"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -36054 -16464 -463 8861 77859
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1499232.9 6520.2 229.94 <0.0000000000000002 ***
## op_count 9850.0 224.7 43.83 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 23620 on 49 degrees of freedom
## Multiple R-squared: 0.9751, Adjusted R-squared: 0.9746
## F-statistic: 1921 on 1 and 49 DF, p-value: < 0.00000000000000022
## [1] "PUSH24" "besu"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -44987 -24338 -11459 7711 161901
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1489749.9 10978.7 135.69 <0.0000000000000002 ***
## op_count 10484.9 378.4 27.71 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 39780 on 49 degrees of freedom
## Multiple R-squared: 0.94, Adjusted R-squared: 0.9388
## F-statistic: 767.7 on 1 and 49 DF, p-value: < 0.00000000000000022
## [1] "PUSH25" "besu"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -55140 -22484 -3919 8339 208132
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1493816.0 11475.3 130.18 <0.0000000000000002 ***
## op_count 10214.3 395.5 25.82 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 41580 on 49 degrees of freedom
## Multiple R-squared: 0.9316, Adjusted R-squared: 0.9302
## F-statistic: 666.9 on 1 and 49 DF, p-value: < 0.00000000000000022
## [1] "PUSH26" "besu"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -59709 -19520 -7189 8602 97079
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1515269.7 10255.4 147.75 <0.0000000000000002 ***
## op_count 9485.9 353.5 26.84 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 37160 on 49 degrees of freedom
## Multiple R-squared: 0.9363, Adjusted R-squared: 0.935
## F-statistic: 720.1 on 1 and 49 DF, p-value: < 0.00000000000000022
## [1] "PUSH27" "besu"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -51577 -26431 -17466 -1291 435354
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1480819.0 19294.9 76.75 <0.0000000000000002 ***
## op_count 10794.2 665.1 16.23 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 69910 on 49 degrees of freedom
## Multiple R-squared: 0.8432, Adjusted R-squared: 0.84
## F-statistic: 263.4 on 1 and 49 DF, p-value: < 0.00000000000000022
## [1] "PUSH28" "besu"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -64972 -21444 -7446 14106 119394
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1509046.0 10817.9 139.50 <0.0000000000000002 ***
## op_count 9590.1 372.9 25.72 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 39200 on 49 degrees of freedom
## Multiple R-squared: 0.931, Adjusted R-squared: 0.9296
## F-statistic: 661.5 on 1 and 49 DF, p-value: < 0.00000000000000022
## [1] "PUSH29" "besu"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -59256 -19057 -5733 16468 74979
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1482635.7 7390.9 200.60 <0.0000000000000002 ***
## op_count 10689.8 254.8 41.96 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 26780 on 49 degrees of freedom
## Multiple R-squared: 0.9729, Adjusted R-squared: 0.9724
## F-statistic: 1761 on 1 and 49 DF, p-value: < 0.00000000000000022
## [1] "PUSH30" "besu"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -48360 -20130 -5951 10287 95972
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1479733.2 7946.1 186.22 <0.0000000000000002 ***
## op_count 10685.7 273.9 39.01 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 28790 on 49 degrees of freedom
## Multiple R-squared: 0.9688, Adjusted R-squared: 0.9682
## F-statistic: 1522 on 1 and 49 DF, p-value: < 0.00000000000000022
## [1] "PUSH31" "besu"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -41676 -27439 -13579 7200 154591
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1484080.2 12399.2 119.69 <0.0000000000000002 ***
## op_count 10617.1 427.4 24.84 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 44930 on 49 degrees of freedom
## Multiple R-squared: 0.9264, Adjusted R-squared: 0.9249
## F-statistic: 617.1 on 1 and 49 DF, p-value: < 0.00000000000000022
## [1] "PUSH32" "besu"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -65492 -23675 -2688 13288 121836
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1519434.9 10248.9 148.25 <0.0000000000000002 ***
## op_count 9665.5 353.3 27.36 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 37140 on 49 degrees of freedom
## Multiple R-squared: 0.9386, Adjusted R-squared: 0.9373
## F-statistic: 748.6 on 1 and 49 DF, p-value: < 0.00000000000000022
## [1] "DUP1" "besu"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -25801 -10766 -3931 8029 62981
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3442906.7 4943.0 696.53 <0.0000000000000002 ***
## op_count 9825.2 170.4 57.67 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 17910 on 49 degrees of freedom
## Multiple R-squared: 0.9855, Adjusted R-squared: 0.9852
## F-statistic: 3325 on 1 and 49 DF, p-value: < 0.00000000000000022
## [1] "DUP2" "besu"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -35096 -13964 -2306 12303 55312
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3447794.2 5055.0 682.05 <0.0000000000000002 ***
## op_count 9906.9 174.2 56.86 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 18320 on 49 degrees of freedom
## Multiple R-squared: 0.9851, Adjusted R-squared: 0.9848
## F-statistic: 3233 on 1 and 49 DF, p-value: < 0.00000000000000022
## [1] "DUP3" "besu"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -44475 -20348 -4020 3815 295052
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3452560.7 13023.8 265.10 <0.0000000000000002 ***
## op_count 9988.6 448.9 22.25 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 47190 on 49 degrees of freedom
## Multiple R-squared: 0.9099, Adjusted R-squared: 0.9081
## F-statistic: 495.1 on 1 and 49 DF, p-value: < 0.00000000000000022
## [1] "DUP4" "besu"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -33441 -10854 -891 10290 97391
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3443469.6 5848.8 588.8 <0.0000000000000002 ***
## op_count 10020.7 201.6 49.7 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 21190 on 49 degrees of freedom
## Multiple R-squared: 0.9806, Adjusted R-squared: 0.9802
## F-statistic: 2471 on 1 and 49 DF, p-value: < 0.00000000000000022
## [1] "DUP5" "besu"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -61663 -37491 -30644 -14776 1345295
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3475926 53791 64.619 < 0.0000000000000002 ***
## op_count 9526 1854 5.138 0.00000481 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 194900 on 49 degrees of freedom
## Multiple R-squared: 0.3501, Adjusted R-squared: 0.3368
## F-statistic: 26.4 on 1 and 49 DF, p-value: 0.000004814
## [1] "DUP6" "besu"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -56134 -9156 -2424 14362 33387
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3443568.3 4978.1 691.7 <0.0000000000000002 ***
## op_count 9849.7 171.6 57.4 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 18040 on 49 degrees of freedom
## Multiple R-squared: 0.9853, Adjusted R-squared: 0.985
## F-statistic: 3295 on 1 and 49 DF, p-value: < 0.00000000000000022
## [1] "DUP7" "besu"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -35337 -12671 2008 10266 55821
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3466946 5078 682.7 <0.0000000000000002 ***
## op_count 10047 175 57.4 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 18400 on 49 degrees of freedom
## Multiple R-squared: 0.9853, Adjusted R-squared: 0.985
## F-statistic: 3294 on 1 and 49 DF, p-value: < 0.00000000000000022
## [1] "DUP8" "besu"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -50292 -18946 -5360 4074 364459
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3492118.1 15299.9 228.24 <0.0000000000000002 ***
## op_count 10171.1 527.4 19.29 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 55440 on 49 degrees of freedom
## Multiple R-squared: 0.8836, Adjusted R-squared: 0.8812
## F-statistic: 372 on 1 and 49 DF, p-value: < 0.00000000000000022
## [1] "DUP9" "besu"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -43173 -14314 -1954 9056 139525
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3444313.7 7763.9 443.63 <0.0000000000000002 ***
## op_count 9917.3 267.6 37.06 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 28130 on 49 degrees of freedom
## Multiple R-squared: 0.9655, Adjusted R-squared: 0.9648
## F-statistic: 1373 on 1 and 49 DF, p-value: < 0.00000000000000022
## [1] "DUP10" "besu"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -45362 -11402 -2309 9626 54325
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3450140.4 5649.3 610.7 <0.0000000000000002 ***
## op_count 9775.8 194.7 50.2 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 20470 on 49 degrees of freedom
## Multiple R-squared: 0.9809, Adjusted R-squared: 0.9805
## F-statistic: 2520 on 1 and 49 DF, p-value: < 0.00000000000000022
## [1] "DUP11" "besu"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -37377 -11627 -4189 10525 60547
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3520456.8 5036.9 698.93 <0.0000000000000002 ***
## op_count 10310.3 173.6 59.38 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 18250 on 49 degrees of freedom
## Multiple R-squared: 0.9863, Adjusted R-squared: 0.986
## F-statistic: 3527 on 1 and 49 DF, p-value: < 0.00000000000000022
## [1] "DUP12" "besu"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -40729 -12739 1660 9667 36309
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3440531.8 3961.1 868.57 <0.0000000000000002 ***
## op_count 10094.2 136.5 73.93 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 14350 on 49 degrees of freedom
## Multiple R-squared: 0.9911, Adjusted R-squared: 0.9909
## F-statistic: 5466 on 1 and 49 DF, p-value: < 0.00000000000000022
## [1] "DUP13" "besu"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -58951 -28302 -12532 8985 516321
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3442568.7 21537.5 159.84 <0.0000000000000002 ***
## op_count 11290.2 742.4 15.21 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 78040 on 49 degrees of freedom
## Multiple R-squared: 0.8252, Adjusted R-squared: 0.8216
## F-statistic: 231.3 on 1 and 49 DF, p-value: < 0.00000000000000022
## [1] "DUP14" "besu"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -41808 -14007 -507 6993 225773
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3469582.1 10050.8 345.20 <0.0000000000000002 ***
## op_count 10006.8 346.4 28.88 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 36420 on 49 degrees of freedom
## Multiple R-squared: 0.9445, Adjusted R-squared: 0.9434
## F-statistic: 834.3 on 1 and 49 DF, p-value: < 0.00000000000000022
## [1] "DUP15" "besu"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -38695 -12769 -846 13187 40955
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3431051.0 4630.3 741.01 <0.0000000000000002 ***
## op_count 10242.8 159.6 64.18 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 16780 on 49 degrees of freedom
## Multiple R-squared: 0.9882, Adjusted R-squared: 0.988
## F-statistic: 4119 on 1 and 49 DF, p-value: < 0.00000000000000022
## [1] "DUP16" "besu"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -37688 -9731 -478 11319 42984
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3588643.6 5286.2 678.87 <0.0000000000000002 ***
## op_count 9965.8 182.2 54.69 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 19150 on 49 degrees of freedom
## Multiple R-squared: 0.9839, Adjusted R-squared: 0.9836
## F-statistic: 2991 on 1 and 49 DF, p-value: < 0.00000000000000022
## [1] "SWAP1" "besu"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -181296 -93101 -16912 83599 200933
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1863654 29974 62.17 <0.0000000000000002 ***
## op_count 23659 1033 22.90 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 108600 on 49 degrees of freedom
## Multiple R-squared: 0.9145, Adjusted R-squared: 0.9128
## F-statistic: 524.4 on 1 and 49 DF, p-value: < 0.00000000000000022
## [1] "SWAP2" "besu"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -177611 -88786 -12901 84691 212435
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1872646 30310 61.78 <0.0000000000000002 ***
## op_count 23452 1045 22.45 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 109800 on 49 degrees of freedom
## Multiple R-squared: 0.9114, Adjusted R-squared: 0.9096
## F-statistic: 503.9 on 1 and 49 DF, p-value: < 0.00000000000000022
## [1] "SWAP3" "besu"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -156513 -89011 -10280 79202 203654
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1865922 30198 61.79 <0.0000000000000002 ***
## op_count 23799 1041 22.86 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 109400 on 49 degrees of freedom
## Multiple R-squared: 0.9143, Adjusted R-squared: 0.9125
## F-statistic: 522.7 on 1 and 49 DF, p-value: < 0.00000000000000022
## [1] "SWAP4" "besu"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -201256 -75574 -29902 75160 206025
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1874799 29664 63.20 <0.0000000000000002 ***
## op_count 23382 1022 22.87 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 107500 on 49 degrees of freedom
## Multiple R-squared: 0.9143, Adjusted R-squared: 0.9126
## F-statistic: 522.9 on 1 and 49 DF, p-value: < 0.00000000000000022
## [1] "SWAP5" "besu"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -177788 -87287 -24681 80487 196629
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1879756 29348 64.05 <0.0000000000000002 ***
## op_count 23166 1012 22.90 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 106300 on 49 degrees of freedom
## Multiple R-squared: 0.9145, Adjusted R-squared: 0.9128
## F-statistic: 524.4 on 1 and 49 DF, p-value: < 0.00000000000000022
## [1] "SWAP6" "besu"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -189494 -81037 -5955 68950 201945
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1892192 30336 62.38 <0.0000000000000002 ***
## op_count 23409 1046 22.39 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 109900 on 49 degrees of freedom
## Multiple R-squared: 0.9109, Adjusted R-squared: 0.9091
## F-statistic: 501.2 on 1 and 49 DF, p-value: < 0.00000000000000022
## [1] "SWAP7" "besu"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -202321 -76853 -11091 81967 201991
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1902890 29931 63.58 <0.0000000000000002 ***
## op_count 23840 1032 23.11 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 108500 on 49 degrees of freedom
## Multiple R-squared: 0.9159, Adjusted R-squared: 0.9142
## F-statistic: 534 on 1 and 49 DF, p-value: < 0.00000000000000022
## [1] "SWAP8" "besu"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -187989 -79170 -19634 90768 223166
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1869643 29851 62.63 <0.0000000000000002 ***
## op_count 23572 1029 22.91 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 108200 on 49 degrees of freedom
## Multiple R-squared: 0.9146, Adjusted R-squared: 0.9129
## F-statistic: 524.8 on 1 and 49 DF, p-value: < 0.00000000000000022
## [1] "SWAP9" "besu"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -190589 -81888 -9541 90663 197708
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1886731 30005 62.88 <0.0000000000000002 ***
## op_count 23022 1034 22.26 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 108700 on 49 degrees of freedom
## Multiple R-squared: 0.91, Adjusted R-squared: 0.9082
## F-statistic: 495.5 on 1 and 49 DF, p-value: < 0.00000000000000022
## [1] "SWAP10" "besu"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -192024 -83509 -8871 74270 190474
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1974936.8 27441.8 71.97 <0.0000000000000002 ***
## op_count 22906.0 945.9 24.22 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 99430 on 49 degrees of freedom
## Multiple R-squared: 0.9229, Adjusted R-squared: 0.9213
## F-statistic: 586.4 on 1 and 49 DF, p-value: < 0.00000000000000022
## [1] "SWAP11" "besu"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -187518 -87495 -34970 86269 595068
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1882072 38203 49.27 <0.0000000000000002 ***
## op_count 23400 1317 17.77 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 138400 on 49 degrees of freedom
## Multiple R-squared: 0.8657, Adjusted R-squared: 0.8629
## F-statistic: 315.8 on 1 and 49 DF, p-value: < 0.00000000000000022
## [1] "SWAP12" "besu"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -162779 -91739 -15715 85245 196744
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1913089 29338 65.21 <0.0000000000000002 ***
## op_count 22895 1011 22.64 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 106300 on 49 degrees of freedom
## Multiple R-squared: 0.9127, Adjusted R-squared: 0.911
## F-statistic: 512.6 on 1 and 49 DF, p-value: < 0.00000000000000022
## [1] "SWAP13" "besu"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -204574 -80769 1002 86188 217865
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1884096 31285 60.22 <0.0000000000000002 ***
## op_count 23471 1078 21.77 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 113400 on 49 degrees of freedom
## Multiple R-squared: 0.9063, Adjusted R-squared: 0.9043
## F-statistic: 473.7 on 1 and 49 DF, p-value: < 0.00000000000000022
## [1] "SWAP14" "besu"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -198397 -92042 -36789 102726 464604
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1930287 35748 54.00 <0.0000000000000002 ***
## op_count 21760 1232 17.66 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 129500 on 49 degrees of freedom
## Multiple R-squared: 0.8642, Adjusted R-squared: 0.8614
## F-statistic: 311.8 on 1 and 49 DF, p-value: < 0.00000000000000022
## [1] "SWAP15" "besu"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -163281 -90578 -7210 82103 185135
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2024177.2 28196.5 71.79 <0.0000000000000002 ***
## op_count 22945.6 971.9 23.61 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 102200 on 49 degrees of freedom
## Multiple R-squared: 0.9192, Adjusted R-squared: 0.9175
## F-statistic: 557.4 on 1 and 49 DF, p-value: < 0.00000000000000022
## [1] "SWAP16" "besu"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -223032 -86075 -21051 75833 502957
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1967414 35739 55.05 <0.0000000000000002 ***
## op_count 23040 1232 18.70 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 129500 on 49 degrees of freedom
## Multiple R-squared: 0.8771, Adjusted R-squared: 0.8746
## F-statistic: 349.8 on 1 and 49 DF, p-value: < 0.00000000000000022
Export the results
write.csv(estimates, paste0("../../local/", env, "_marginal_estimated_cost.csv"), quote=FALSE, row.names=FALSE)